221 results about "Event graph" patented technology

The invention discloses a logistics traceability system and method based on a block chain technology, and relates to the technical field of logistics traceability, and the method comprises the steps: constructing an event graph structure containing a plurality of behavior event nodes, and calculating a structure closure signature value; judging whether the original data of the behavior event node is written into the block chain or not according to the responsibility structure weight value; constructing a verification path mapping graph based on the structure closure signature value and the responsibility structure weight value; when a traceability request is received, calling the verification path mapping graph, performing consistency verification on the original data of the target behavior event node and the structure mapping hash value, and generating a traceability verification result; according to the method, the credibility of the behavior event data structure is improved, meanwhile, the on-chain storage cost is effectively reduced, and the technical contradiction between full-uplink data and non-uplink data is solved.

The invention relates to the technical field of computer network management, and discloses an intelligent alarm preprocessing method of an adaptive rule engine, which comprises the following steps: firstly, acquiring real-time operation data of network equipment and a preset alarm baseline, and analyzing a historical alarm sequence through an association rule mining model to obtain the preset baseline; then inputting the data into an alarm decision model based on an event atlas analysis algorithm, processing the data by the model by using a multi-dimensional time window algorithm, calculating adaptive weight correction processing parameters in combination with node resource load parameters, and outputting strategy parameters; and finally, adjusting rule engine judgment logic according to parameters to realize alarm intelligent filtering and aggregation. In addition, a link emergency optimization step is provided. The method improves the accuracy and adaptive capability of alarm processing, and is suitable for a complex network environment.

The invention relates to the technical field of engineering construction supervision, and discloses a building engineering construction supervision system based on big data analysis, and the system comprises a multi-modal data collection module which is used for collecting multi-source data of a construction site, and the multi-source data comprises structure sensor data, environment monitoring data, video image data, construction log data and building information model (BIM) state data; performing standardization processing and time synchronization on the data to generate a construction state data sequence; and the construction event modeling module identifies key events in the construction process based on the construction state data sequence and constructs a construction event graph, and the construction event graph is composed of event nodes representing construction events and event edges representing event collaboration or time correlation. By introducing an event atlas construction mechanism based on multi-source construction data driving, structured expression and semantic association mapping of key behavior units of a construction site are realized, and the problem of insufficient non-structured information processing capability in construction monitoring is overcome.

Systems, apparatuses, and methods for root cause analysis of a computing network are disclosed. A network management system builds a causation model based on causal mappings corresponding to network events. The causal mapping identifies a logical order of occurrence between a given network event and other network events. Network event obtained from the computing network is analyzed using the causation model for performing a root cause analysis for the computing network by generating a network event graph defining a one-to-one relationship between a given network event and one or more other network events. The causation model is built using determined hierarchical relationship of network events with a plurality of network entities as connected within the computing network.

The invention discloses a multi-modal power grid fault diagnosis method and system based on a causal event atlas, and belongs to the technical field of intelligent operation and maintenance of power systems. The method comprises the following steps: preprocessing historical fault case data of power grid equipment, and constructing a causal event atlas database; when a fault diagnosis request is received, analyzing the fault diagnosis request by the planning agent, generating an initial fault hypothesis set in combination with power field knowledge, endowing a corresponding credibility score to the fault hypothesis, retrieving the cause subgraph as evidence in an iterative loop, and updating the credibility score of the fault hypothesis by the reasoning agent; and generating a multi-modal diagnosis report after the termination condition is met. According to the method, the problems of insufficient causal modeling and poor interpretability of a traditional method are solved, and the diagnosis accuracy, efficiency and user credibility are remarkably improved.

The invention relates to the technical field of information, and provides a bulk commodity transaction system based on a block chain, and the system comprises a parameter and publishing module which uniformly receives and stores parameters; the unified examination and approval engine performs isomorphic arrangement and gray level updating through a configurable template and a state machine; consistency snapshots and recovery are paused / abnormally collected snapshots and monotonically replayed, and it is guaranteed that the sequence is consistent with a window; the security fund risk control uses DSL + fund DAG to drive freezing / unfreezing / deduction and account checking; and block chain evidence storage and auditing execute field-level hash and pedigree anchoring to realize verifiable traceability. According to the system, an end-to-end consistency and verifiability framework is constructed around the aspects of field-level fingerprinting, unified examination and approval, windowed stable sorting, consistency snapshot and monotone replay, fund event patterning, contract assembling and signature anchoring, settlement / final recalculation and pedigree uplink and offline verification; and under the conditions of abnormity, recovery and version evolution, each link keeps uniform caliber, stable sorting and approvable state.

An attack detection and source tracing method includes acquiring entities in a target network environment and interaction event information between the entities and constructing a network event graph with the entities and the interaction event information; determining a graph embedding vector of each interaction event information in the network event graph as feature information based on a preset attack feature recognition model and determining an attack event in the network event graph according to the feature information; determining dependencies between the attack event and remaining interaction event information in the network event graph and searching for a corresponding interaction event information as source tracing information of the attack event according to the dependencies.

The invention relates to a computer vision technology, in particular to an event camera target detection method based on Euclidean-hyperbolic motion perception, which comprises the following steps: down-sampling original event data, constructing an event graph by using the down-sampled data, and inputting the event graph into a double-space network for target detection; performing local perception feature extraction on the event graph by using a graph convolutional network based on a B-spline kernel function, and projecting the extracted features to a hyperbolic space; extracting global event dependency features from the local sensing features mapped to the hyperbolic space through a learnable curvature hyperbolic graph convolutional network; and decoding the global event dependency feature and inputting the decoded global event dependency feature into a sensing head, and outputting a target detection result by the sensing head. The method solves the problems of noise suppression, topological mapping and hierarchical expression in event data, has good application prospects and popularization value, and is particularly suitable for real-time target sensing tasks in complex dynamic environments such as automatic driving and robot navigation.

The invention relates to the related technical field of computer vision, in particular to an event-guided image motion deblurring method in a real scene, which comprises the following steps of: 1, constructing an event-image two-branch fusion deblurring network, and obtaining effective information in a blurred image and an event stream to realize motion blurred image restoration; and 2, in the event branch, converting a spatially sparse event stream into a dense three-dimensional tensor form by using a voxel grid event representation method, and obtaining a spatio-temporal motion feature representation of the event through a spatio-temporal motion enhancement module. According to the event-guided image motion deblurring method in the real scene, an event stream in exposure time is divided into N time slices, the N time slices are respectively accumulated into 2xHxW event frames according to polarities, a 2NxHxW three-dimensional event tensor is constructed, time sequence dependency in 2N event channels is modeled by adopting a bidirectional channel scanning strategy based on a selection state space model, and the time sequence dependency in the 2N event channels is calculated. Information of motion over time over the exposure time is modeled.

Dynamic graph prediction can be achieved using a statistical model of graph dynamics that combines neural networks, including graph neural networks (GNNs), with maximum likelihood estimation. More specifically, in some embodiments, a GNN is used to compute graph embeddings representing an evolving graph at two different points in time, an additional neural network is used to create a forward image of the graph embedding associated with the first point in time, and a statistical distribution of the difference between the forward image and the graph embedding associated with the second point in time is evaluated. The GNN, additional neural network, and statistical distribution collectively constitute the statistical model, which can be trained on training data comprising pairs of graphs from one or more time series of evolving graphs. Such a statistical model may be employed, for example, to predict graph changes in a cybersecurity incident graph.

The invention discloses a laboratory risk control method based on a graph neural network, and the method comprises the following steps: S1, constructing a dynamically updated interaction event graph based on an interaction event sequence of laboratory equipment, personnel and environment; s2, dynamically updating the state space of each node according to the event preorder relation, and generating a synchronous node state sequence; s3, generating an early warning feature set of a node state change trend by adopting a graph convolution operation; s4, constructing an inter-node risk incidence matrix according to the early warning feature set, and generating an updated risk propagation path; s5, evaluating the probability of transition from the node state to the abnormal state, and generating a state risk prediction value; and S6, inputting a wolf pack optimization algorithm, and optimizing a parameter combination to obtain a laboratory risk prediction result. According to the invention, the real-time performance and accuracy of laboratory risk prediction are improved.

The invention discloses a power grid dispatching report generation method based on natural language processing, relates to the technical field of power grid dispatching report processing, and aims at solving the problems that a traditional report generation mode is low in efficiency and large in subjective deviation. The method comprises the steps that multi-source data are integrated, the data are preprocessed, and multi-dimensional time sequence data are output; generating an event atlas based on the high-dimensional data abstract and the state recognition result; converting the structured data into basic natural language expression, and filling dynamic content; generating a report text; generating a dynamic visual chart and an image-text report based on the generated report text, the event graph and trend data generated by the time sequence analysis model; an image-text report is adopted, the report is divided into key suggestions and detail descriptions, and highlighted high-risk event prompts are generated. According to the method, the report generation efficiency can be improved, subjective deviation in manual analysis is eliminated, and objectivity and specialty of report content are ensured.

The invention belongs to the field of network security, particularly relates to a network security event analysis method, system and equipment, and aims to solve the problem that the existing network security event analysis and detection technology is poor in detection effect. Comprising the following steps: determining common data and suspicious data in a network security data set; constructing a multi-dimensional base line for the suspicious data, and generating a composite feature vector in combination with the feature vector of the common data; identifying an abnormal line in the composite feature vector as an abnormal event node; constructing an event graph based on the relationship between different target devices, the relationship between the target device and the user, and the relationship between the target device and the abnormal event node; determining an event type based on a node feature matrix of the event graph; and generating a network security event analysis result based on the event type, the abnormal score, the event occurrence time, the event occurrence position and the attack path. The network security event analysis method and the network security event analysis device have stronger generalization ability for novel attacks, and improve the accuracy of network security event analysis.

The invention discloses a field event element extraction method and system based on prompt learning and knowledge completion, and the method comprises the steps: converting an extraction task into a constrained generation problem through structured prompt, achieving the stable extraction under a small number of labeled samples through the existing knowledge prior of a large language model, reducing the dependence on large-scale labeled data, and improving the extraction efficiency. And the cross-domain adaptive capacity is improved. A vectorization knowledge retrieval mechanism is introduced, related evidences are obtained from an external knowledge source, elements which are not clearly expressed in a text are complemented, the integrity of information is enhanced, and the accuracy and credibility of a result are improved through knowledge verification. And through a self-adaptive fusion mechanism of prompt and knowledge, the model can flexibly balance text context and external knowledge, and the robustness of complex context and fuzzy expression is improved. And finally, standardized and structured element data are output, a convenient interface is provided for downstream event atlas construction and analysis tasks, and the automation degree of domain knowledge processing and the system integration efficiency are improved.

The invention relates to the technical field of computers, and provides a log anomaly detection method and device, equipment, a storage medium and a program product. The method comprises the steps of constructing a time sequence event graph based on a log event sequence; determining an adjacent matrix of each event graph snapshot and a node feature matrix of each event graph snapshot; inputting each event graph snapshot, each adjacent matrix and each node feature matrix into a dynamic graph model, and performing time sequence feature extraction and link prediction to obtain a predicted event graph snapshot; and judging whether the log event sequence is abnormal or not based on the predicted event graph snapshot. By means of the mode, efficient detection and positioning of the abnormal log can be achieved, the detection effect of the abnormal log is improved, and the detection requirement of an information system for log abnormity is met.

The invention discloses a smart city event perception and emergency scheduling method based on artificial intelligence. The method comprises the following steps: S1, constructing a standardized structured sample; s2, forming a city sub-event graph; s3, constructing a Bayesian event inference network, and synchronously recording intermediate layer feature response; s4, outputting a dynamic thermodynamic diagram as an event perception model; s5, constructing a multi-target scheduling graph, and generating an emergency scheduling task; s6, taking training of a lightweight student model as a target, constructing a fusion type knowledge distillation mechanism for edge deployment, and fusing teacher model output, intermediate layer feature response and Bayesian causal structure consistency as a distillation target; and S7, adjusting an event state association structure in the Bayesian event inference network and a response weight distribution mechanism of the lightweight student model. The method has the advantages of high reasoning precision, low response delay and edge deployment.

The invention provides a monocular depth estimation method and product based on convolution compensation dual-channel self-attention, and relates to the field of computer vision. Comprising the following steps: converting an event flow of a target scene into three-dimensional tensor representation; obtaining event image fusion multi-scale spatial features based on the image of the target scene and the three-dimensional tensor representation; modeling spatial context correlation in a spatial dimension by utilizing event image fusion multi-scale spatial features through a context modeling self-attention branch to obtain a context modeling self-attention result; through a modal fusion self-attention branch, using the event image to fuse the modal correlation of the multi-scale spatial features in the channel dimension modeling image and the event, and obtaining a modal fusion self-attention result; and pixel-level depth prediction is carried out by using a context modeling self-attention result and a modal fusion self-attention result to obtain a depth map, so that complementary characteristics between an event and an image are fully mined, fine-grained depth fusion expression is realized, and depth estimation precision and generalization ability are effectively improved.

The invention discloses a risk level assessment method based on a graph neural network, and belongs to the technical field of artificial intelligence and machine learning. The method comprises the following steps: firstly, detecting a plurality of events, evaluating risk levels, and cleaning data to construct a risk data set; secondly, based on a feature extractor, extracting features of the event nodes and the index nodes as high-dimensional vectors, and constructing an event graph by taking months as units; and then constructing a graph convolutional neural network, performing training based on a graph data set to obtain a risk level evaluation model based on a month event graph, and finally performing risk level evaluation by using the trained model. According to the method, risk level prediction is completed by applying the graph neural network technology, the expressive force and the operation efficiency of the model can be improved by adopting the combination of the GCN architecture and the readout layer, and the learning ability of the model is also enhanced.

The invention provides a security operation and maintenance auditing system, which collects original log data generated by a plurality of system components through a log auditing unit, carries out structured processing based on the original log data to obtain a plurality of event triples, and constructs an event graph database based on the event triples. Traversing the event graph database based on a preset rule base at a preset time point to generate an attack behavior chain; a vulnerability scanning unit determines a current risk level of a target asset based on the target asset involved in the attack behavior chain and a log behavior density, an event type, a historical risk level and an abnormal operation frequency corresponding to the target asset in a preset time period, and configures a scanning task scheduling parameter based on the current risk level of the target asset, executing a vulnerability scanning task for the target asset based on the scanning task scheduling parameter; the operation and maintenance auditing unit generates the operation and maintenance request based on the attack behavior chain and / or the execution result of the vulnerability scanning task of each asset, so that the system security is effectively improved.

The invention discloses an intelligent fire-fighting hidden danger identification method based on multi-modal fusion, and the method comprises the steps: collecting multi-source heterogeneous data in a building environment, and carrying out the unified processing of the data, and obtaining standardized multi-modal original input data; event anchor point detection is carried out on the multi-modal input, key events are extracted, and a corresponding multi-modal event sequence is generated; constructing an event graph containing time and causal edges, and outputting an alignment event stream by using a space-time coupling causal alignment module; importing BIM and air duct structure information to establish a spatial topological graph, and fusing event streams to generate spatial constraint features; constructing a self-evolution semantic decision map based on the fusion features, and dynamically adjusting node weights and edge connection relationships; and carrying out hidden danger identification and grade division on the real-time data by using the decision map, and outputting an early warning signal and positioning information. According to the method, high-precision identification and intelligent early warning of fire-fighting hidden dangers are realized through space-time causal alignment, spatial topology fusion and self-evolution semantic decision of multi-modal data.

The invention discloses a news event prediction method based on heterogeneous evolutionary event clustering, which comprises the following steps: generating event representations based on preliminarily updated entity representations and relationship representations in a constructed entity graph, and constructing an event graph by taking events as nodes and taking heterogeneous relationships between the events as edges; obtaining event clusters through fuzzy clustering and constructing an event cluster graph; using a self-supervised optimization algorithm to optimize the event cluster representation according to the distance and similarity between the event clusters on the event cluster graph; capturing implicit correlation among the event clusters by using an implicit relation encoder, and sequentially updating representation of the event clusters, representation of events, and representation of entities and relations after sparsification and information aggregation; and predicting through the news event model based on convolution. According to the method, the pairwise correlation, the high-order correlation and the multi-step time sequence evolution of the events are effectively modeled, and the method has important application value in the aspects of international situation analysis, social governance, intelligent decision support and the like.

The invention relates to the technical field of computer vision, in particular to an event image fusion semantic segmentation method based on lightweight pulse driving. The method comprises the following steps: S1, acquiring image data and corresponding event data; s2, preprocessing the event data; s3, inputting the image data and the processed event data into a pulse encoder; and S4, inputting the shallow-deep features extracted by the pulse encoder into a pulse decoder to obtain a semantic segmentation result. According to the method, the advantages of high time resolution and high dynamic range of event data and the advantage of rich static details of image data are fully utilized, and accurate semantic segmentation is realized; meanwhile, due to the adoption of the pulse neural network, the method also has the characteristics of high calculation efficiency and low energy consumption.

The invention discloses a municipal social risk event evolution analysis and intelligent prediction method based on a affair graph, relates to the technical field of artificial intelligence, and solves the problem of lack of hierarchical analysis of municipal social risk event development. The technical problem that it is difficult to more accurately analyze and predict different levels of development of different urban social risk events due to establishment of a social dispute atlas for structuring the levels of development of the urban social risk events is also lacked. Through the development level of the city social risk events, the time sequence and importance of the city social risk events can be clearly displayed, so that the information is easier to understand and analyze. By linking the dispute subject node and the content node which are associated with each other, common features in similar city social risk events can be conveniently identified, and data support is provided for subsequent prediction; by establishing the associated edge and the unassociated edge, the relationship among the nodes can be clearly displayed.

The invention provides an inter-country network attack prediction method and system based on GNN and LLM fusion, and is applied to the field of network security. On the basis of a news event data set, a big language model is used for carrying out content analysis on a news text, in combination with network attack historical data, military facility change features in a satellite image are converted into text semantic embedding through a cross-modal alignment module, and a network attack prediction-oriented multi-modal data set is generated; the multi-modal data set is processed, layering is carried out according to time granularity, each layer of graph comprises country nodes, event nodes and relation nodes, features of different levels are aggregated through a dynamic time window, and a target directed multi-view dynamic graph composed of network attacks, news events, image events and public opinion events is generated; and processing the target event set based on the big language model in combination with the semantic information of the fusion graph data and the text data, and generating inter-country network attack prediction result information.

The invention discloses an event aggregation-based short video information detection method, and relates to the technical field of network space security information detection. The method comprises the following steps: acquiring all short videos and multi-modal information thereof under a target event, and extracting the characteristics of the event, the videos and the multi-modal information; constructing an event graph which comprises event, video and modal nodes, and establishing corresponding edges according to inclusion relations and similarities among the nodes; performing graph coding on the event graph by adopting a heterogeneous graph neural network based on a double-layer attention mechanism to obtain feature representations of event nodes and video nodes; and guiding video-level detection in combination with the event-level detection result to obtain a detection result of the short video. According to the method, the complementation and mutual exclusion relationship between videos under the same event is effectively mined through event graph reasoning, the feature representation of video nodes is remarkably optimized, and the accuracy of short video information detection is greatly improved.

The invention discloses a video event association reasoning method based on a knowledge graph. The method comprises the following steps: S1, collecting video data and preprocessing the video data; s2, constructing an event graph according to a preprocessing result, and generating a state representation sequence through a graph convolutional network; s3, constructing a DreamerV3 world model, inputting a state representation sequence, and generating an event trajectory set; s4, performing trajectory value evaluation operation on the event trajectory set, and constructing a target path based on an evaluation result; s5, executing a structure alignment operation in the event graph according to the target path, and generating a reasoning knowledge graph; and S6, based on the inference knowledge graph, outputting a video event association result according to a time sequence. According to the method, video identification, map modeling and trajectory prediction methods are fully fused, and an efficient closed loop of key event extraction and map-level causal reasoning is realized.

An attack detection and traceback method and apparatus, and an electronic device and a storage medium. The method comprises: acquiring entities in a target network environment and interaction event information between the entities, and constructing a network event graph from the entities and the interaction event information; on the basis of a preset attack feature recognition model, determining graph embedding vectors of each piece of interaction event information in the network event graph as feature information, and on the basis of the feature information, determining an attack event in the network event graph; and determining the degree of dependency between the attack event and remaining interaction event information in the network event graph, and on the basis of the degree of dependency, searching for corresponding interaction event information as traceback information for the attack event.

The invention discloses a digital work card-driven business window multi-source recording data fusion intelligent analysis system, and particularly relates to the technical field of voice processing. The system is based on asynchronous audio streams collected by a wearable digital work card, an array microphone and environment pickup equipment; structured alignment and unified time reference construction of multi-source recording data are realized by adopting the steps of anchor point detection, event graph construction, cross-source matching, elastic time distortion alignment and the like. The system completes cross-source event marking by introducing multiple types of anchor point detectors (semantic keywords, acoustic abrupt changes and prompt tones); constructing an event alignment graph and executing confidence classification in combination with a unified clipping and cross sliding matching strategy in the anchor point pairing process; and then performing multi-scale alignment and resampling on the audio stream based on a layered elastic time warping method, and finally outputting a frame-level synchronous voice data stream to provide unified time support for downstream speaker separation, behavior auditing and semantic mapping.

The invention discloses an enterprise knowledge base content retrieval method and device based on an event graph, equipment and a storage medium, and relates to the technical field of natural language process.The method comprises the steps that candidate event information is extracted from an initial enterprise knowledge base text to obtain a structured event sentence set, all the candidate event information in the event sentence set is set as nodes, and the nodes are stored in the enterprise knowledge base text; setting a time sequence relationship among the candidate event information as an edge to obtain an event graph; analyzing user query content to obtain a query field, determining a candidate node set based on the query field and the event graph, performing path search in the candidate node set to obtain a candidate event path, and performing time-aware reasoning on the candidate event path based on a preset time chain thinking prompt strategy to obtain a reasoning result; and performing structured analysis, event path alignment and confidence evaluation processing on the reasoning result to obtain an enterprise knowledge base retrieval text with a reference basis, a time label and a confidence label so as to improve the efficiency of retrieving the content of the enterprise knowledge base.

The invention discloses a monitoring and early warning system and method based on an intelligent door lock, and relates to the technical field of intelligent safety, and the method comprises the steps: initializing a dynamic event graph network, generating event nodes and event association edges according to the real-time operation data of the intelligent door lock and intelligent equipment, and building an event graph structure; analyzing an association mode between the event nodes based on the event graph structure, identifying an abnormal cooperation relationship and forming an abnormal cooperation relationship list; according to the multi-level early warning signal, the intelligent device is linked to execute warning operation; and updating the dynamic event graph network in real time according to feedback data of the warning operation, and optimizing weight configuration of event nodes and event association edges. According to the method, the structure of the event graph can be optimized in real time by adopting a quantum weight superposition and dynamic adjustment method, so that a dynamically changing environment is effectively dealt with, potential safety hazards are accurately identified, and accurate early warning is performed on abnormal behaviors.